| Summary: | A perennial challenge for the characterization and modelling of phenomena involving granular media is that the internal connectivity of, and interactions between, the pores and the particles exhibit hallmarks of complexity: multi-scale and nonlinear interactions that lead to a plethora of patterns at the mesoscale, including fluid flow patterns that ultimately render a permeability of the granular media at the macroscale. A multitude of physical parameters exist to characterize geometry and structure, including pore/particle shape, volume and surface area, while a rich class of complex network parameters quantifies internal connectivity of the pore and particles in the material. A large collection of such variables is likely to exhibit a high degree of redundancy. Here we demonstrate how to use feature selection in machine learning theory to identify the most informative and non-redundant, yet parsimonious set of features that optimally characterizes the interstitial flow properties of porous, granular media, e.g., permeability, from high resolution data.
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